An Algorithm for the Diagnosis of Behcet Disease Uveitis in Adults
Yazar
Tugal-Tutkun, Ilknur
Onal, Sumru
Stanford, Miles
AKMAN, MEHMET
Twisk, Jos W. R.
Boers, Maarten
Ozdal, Pinar Cakar
KADAYIFÇILAR, SİBEL
Amer, Radgonde
Rathinam, Sivakumar R.
Vedhanayaki, Rajesh
Khairallah, Moncef
Akova, Yonca
YALÇINDAĞ, FATİME NİLÜFER
Kardes, Esra
Basarir, Berna
Altan, Cigdem
Ozyazgan, Yilmaz
Gül, Ahmet
Oray, Merih
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Purpose: To develop an algorithm for the diagnosis of Behcet's disease (BD) uveitis based on ocular findings. Methods: Following an initial survey among uveitis experts, we collected multi-center retrospective data on 211 patients with BD uveitis and 207 patients with other uveitides, and identified ocular findings with a high diagnostic odds ratio (DOR). Subsequently, we collected multi-center prospective data on 127 patients with BD uveitis and 322 controls and developed a diagnostic algorithm using Classification and Regression Tree (CART) analysis and expert opinion. Results: We identified 10 items with DOR >5. The items that provided the highest accuracy in CART analysis included superficial retinal infiltrate, signs of occlusive retinal vasculitis, and diffuse retinal capillary leakage as well as the absence of granulomatous anterior uveitis or choroiditis in patients with vitritis. Conclusion: This study provides a diagnostic tree for BD uveitis that needs to be validated in future studies.
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